exposureEM: Combined-Exposure Models by EM and Marquardt Optimization
Fits general two-component combined-exposure models for binary
event histories when the event setting is not observed.
The observed binary event is represented as the union of two latent
component-specific binary events. Known exposure proportions enter as
offsets. Parameters can be estimated by expectation-maximization, direct
Marquardt-damped Newton-Raphson maximization of the observed likelihood, or
a hybrid that uses several expectation-maximization iterations before
direct optimization. Uncertainty is estimated with Louis' formula for the
expectation-maximization estimator and the inverse observed Hessian for
direct and hybrid fits. Complementary log-log, logit, and log component
links are available for all three estimation methods.
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